OVERVIEW OF RUN CHART

Purpose

In-depth view into Run Charts--a quality improvement technique; how Run charts are used to monitor processes; how using Run charts can lead to improved process quality

Usage

Run charts are used to analyze processes according to time or order. Run charts are useful in discovering patterns that occur over time.

Key Terms

Trends:

Trends are patterns or shifts according to time. An upward trend, for instance, would contain a section of data points that increased as time passed.

Population:

A population is the entire data set of the process. If a process produces one thousand parts a day, the population would be the one thousand items.

Sample:

A sample is a subgroup or small portion of the population that is examined when the entire population can not be evaluated. For instance, if the process does produce one thousand items a day, the sample size could be perhaps three hundred.

History

Run charts originated from control charts, which were initially designed by Walter Shewhart. Walter Shewhart was a statistician at Bell Telephone Laboratories in New York. Shewhart developed a system for bringing processes into statistical control by developing ideas which would allow for a system to be controlled using control charts. Run charts evolved from the development of these control charts, but run charts focus more on time patterns while a control chart focuses more on acceptable limits of the process. Shewhart's discoveries are the basis of what as known as SQC or Statistical Quality Control.

INSTRUCTIONS FOR CREATING A CHART

Step 1 : Gathering Data

To begin any run chart, some type of process or operation must be available to take measurements for analysis. Measurements must be taken over a period of time. The data must be collected in a chronological or sequential form. You may start at any point and end at any point. For best results, at least 25 or more samples must be taken in order to get an accurate run chart.

 Step 2 : Organizing Data

Once the data has been placed in chronological or sequential form, it must be divided into two sets of values x and y. The values for x represent time and the values for y represent the measurements taken from the manufacturing process or operation.

Step 3 : Charting Data

Plot the y values versus the x values by hand or by computer, using an appropriate scale that will make the points on the graph visible. Next, draw vertical lines for the x values to separate time intervals such as weeks. Draw horizontal lines to show where trends in the process or operation occur or will occur.

Step 4 : Interpreting Data

After drawing the horizontal and vertical lines to segment data, interpret the data and draw any conclusions that will be beneficial to the process or operation. Some possible outcomes are:

• Trends in the chart
• Cyclical patterns in the data
• Observations from each time interval are consistent

Problem Scenario

Your boss has found out that some of his cosmetic products are very difficult to pack at room temperature. Hence he wants you to help the packing department to solve their problem. You are given three products to test on, with the same number of quantity and at the same volume. You have therefore decided to monitor the time taken for packing the three products at different temperature.

Step 1: Gathering Data

Collect measurements for time taken to pack the three products at four different temperature. Organize and record the data in chronological or sequential form.

                   30ºC       25ºC     20ºC      15ºC      
	
     Product A     1.5h       2.0h     2.5h      3.0h                   
     Product B     2.5h       2.1h     2.4h      2.7h     
     Product C     1.5h       1.5h     1.5h      1.5h     

Step 2: Organizing Data

Determine what the values for the x (temperature) and y (data, packing time) axis will be.

Step 3: Charting Data

Plot the y values versus the x values by hand or by computer using the appropriate scale. Draw horizontal or vertical lines on the graph where trends or inconsistencies occur.

Step 4: Interpreting Data

Interpret results and draw any conclusions that are important. From the chart, we can conclude that both products A and C are best packed at 30ºC. While product B is best packed at 25ºC.